transfix-mt/scripts/lemmatize_glossary.py

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import nltk
import pandas as pd
from nltk.stem import WordNetLemmatizer
nltk.download('wordnet')
wl = WordNetLemmatizer()
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glossary_path = '~/mt-summit-corpora/glossary.tsv'
glossary = pd.read_csv(glossary_path, sep='\t', header=None, names=['source', 'result'])
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source_lemmatized = []
for word in glossary['source']:
word = nltk.word_tokenize(word)
source_lemmatized.append(' '.join([wl.lemmatize(x) for x in word]))
glossary['source_lem'] = source_lemmatized
glossary = glossary[['source', 'source_lem', 'result']]
glossary.set_index('source_lem')
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glossary.to_csv(glossary_path + '.lemmatized', sep='\t', index=False)